Rolling bearing state monitoring method

Abstract

The invention discloses a rolling bearing state monitoring method. The method comprises the steps of firstly converting vibration signals of a rolling bearing into a binary symbol sequence according to a fluctuation trend between adjacent sequence points, then converting the binary symbol sequence into a word frequency sequence, finally calculating an association coefficient between the word frequency sequence corresponding to an original state and the word frequency sequence of any other state, and taking the association coefficient as a characteristic parameter so as to monitor the operating state of the rolling bearing. In the operating process of the rolling bearing, if the variation of a value of the association coefficient at a certain moment exceeds 20% compared with a value of the association coefficient at a previous moment, the operating state of the rolling bearing is regarded to be changed obvious at the moment, and the moment is regarded as a moment when a fault occurs. The rolling bearing state monitoring method is suitable for processing complex rolling bearing vibration signals, can detect early faults of the rolling bearing accurately and timely, has good noise resistance and robustness and is convenient for engineering applications.

Description

technical field [0001] The invention relates to a bearing, in particular to a rolling bearing state monitoring method, which belongs to the field of rotating machinery state monitoring and fault diagnosis. Background technique [0002] Rolling bearings are the most commonly used rotating parts, and their fault features are usually weak, especially when the rolling bearing fault is in the early stage, its fault features are very difficult to extract. Therefore, early fault detection of rolling bearings is a difficult problem. [0003] At present, the traditional rolling bearing state monitoring methods include manual monitoring method, effective value method and kurtosis method. The above methods are all directly monitoring and analyzing the original signal. The noise and robustness are poor, and it is difficult to detect the early faults of rolling bearings in time and accurately. Contents of the invention [0004] The problem to be solved in the present invention is to ...

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Problems solved by technology

[0003] At present, traditional rolling bearing state monitoring methods include artificial monitoring method, effective value method and kurtosis method. The above methods are all directly monitoring and analyzing the original signal. The noise and robustness are poor, and it is difficult to detect the early faults of rolling bearings in time and accurately

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